



from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging


if TYPE_CHECKING:
	from ...onnx.config import PatchingSpec
	from ...tokenization_utils_base import PreTrainedTokenizerBase


a      :  Any			     =			logging.get_logger(__name__)

a      :  Union[str, Any]			     =			{
    'allenai/longformer-base-4096': 'https://huggingface.co/allenai/longformer-base-4096/resolve/main/config.json',
    'allenai/longformer-large-4096': 'https://huggingface.co/allenai/longformer-large-4096/resolve/main/config.json',
    'allenai/longformer-large-4096-finetuned-triviaqa': (
        'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/config.json'
    ),
    'allenai/longformer-base-4096-extra.pos.embd.only': (
        'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/config.json'
    ),
    'allenai/longformer-large-4096-extra.pos.embd.only': (
        'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/config.json'
    ),
}







class 				_a				(   _lowerCAmelCase					):
			A      		=	'''longformer'''

			def __init__(self,   SCREAMING_SNAKE_CASE_ = 512,   SCREAMING_SNAKE_CASE_ = 2,   SCREAMING_SNAKE_CASE_ = 1,   SCREAMING_SNAKE_CASE_ = 0,   SCREAMING_SNAKE_CASE_ = 2,   SCREAMING_SNAKE_CASE_ = 30522,   SCREAMING_SNAKE_CASE_ = 768,   SCREAMING_SNAKE_CASE_ = 12,   SCREAMING_SNAKE_CASE_ = 12,   SCREAMING_SNAKE_CASE_ = 3072,   SCREAMING_SNAKE_CASE_ = "gelu",   SCREAMING_SNAKE_CASE_ = 0.1,   SCREAMING_SNAKE_CASE_ = 0.1,   SCREAMING_SNAKE_CASE_ = 512,   SCREAMING_SNAKE_CASE_ = 2,   SCREAMING_SNAKE_CASE_ = 0.0_2,   SCREAMING_SNAKE_CASE_ = 1E-12,   SCREAMING_SNAKE_CASE_ = False,   **SCREAMING_SNAKE_CASE_,   )       ->   Any:
						super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_,   **SCREAMING_SNAKE_CASE_					)

						UpperCAmelCase_:		List[str]							      =     attention_window
						UpperCAmelCase_:		Optional[int]							      =     sep_token_id
						UpperCAmelCase_:		str							      =     bos_token_id
						UpperCAmelCase_:		Dict							      =     eos_token_id
						UpperCAmelCase_:		Dict							      =     vocab_size
						UpperCAmelCase_:		Tuple							      =     hidden_size
						UpperCAmelCase_:		Any							      =     num_hidden_layers
						UpperCAmelCase_:		List[Any]							      =     num_attention_heads
						UpperCAmelCase_:		Tuple							      =     hidden_act
						UpperCAmelCase_:		str							      =     intermediate_size
						UpperCAmelCase_:		Union[str, Any]							      =     hidden_dropout_prob
						UpperCAmelCase_:		Dict							      =     attention_probs_dropout_prob
						UpperCAmelCase_:		Optional[Any]							      =     max_position_embeddings
						UpperCAmelCase_:		Union[str, Any]							      =     type_vocab_size
						UpperCAmelCase_:		Dict							      =     initializer_range
						UpperCAmelCase_:		Dict							      =     layer_norm_eps
						UpperCAmelCase_:		str							      =     onnx_export







class 				_a				(   _lowerCAmelCase					):

			def __init__(self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_ = "default",   SCREAMING_SNAKE_CASE_ = None					)       ->   List[Any]:
						super().__init__(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		List[str]							      =     True

			@property
			def      __snake_case       (self					)       ->   Mapping[str, Mapping[int, str]]:
						if self.task == "multiple-choice":
									UpperCAmelCase_:		int							      =     {0: """batch""", 1: """choice""", 2: """sequence"""}
						else:
									UpperCAmelCase_:		List[str]							      =     {0: """batch""", 1: """sequence"""}
						return OrderedDict(
						    [
						        ("""input_ids""", dynamic_axis),
						        ("""attention_mask""", dynamic_axis),
						        ("""global_attention_mask""", dynamic_axis),
						    ]					)

			@property
			def      __snake_case       (self					)       ->   Mapping[str, Mapping[int, str]]:
						UpperCAmelCase_:		str							      =     super().outputs
						if self.task == "default":
									UpperCAmelCase_:		List[str]							      =     {0: """batch"""}
						return outputs

			@property
			def      __snake_case       (self					)       ->   float:
						return 1E-4

			@property
			def      __snake_case       (self					)       ->   int:
						# needs to be >= 14 to support tril operator
						return max(super().default_onnx_opset,   14					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_ = -1,   SCREAMING_SNAKE_CASE_ = -1,   SCREAMING_SNAKE_CASE_ = False,   SCREAMING_SNAKE_CASE_ = None,   )       ->   Mapping[str, Any]:
						UpperCAmelCase_:		int							      =     super().generate_dummy_inputs(
						    preprocessor=SCREAMING_SNAKE_CASE_,   batch_size=SCREAMING_SNAKE_CASE_,   seq_length=SCREAMING_SNAKE_CASE_,   is_pair=SCREAMING_SNAKE_CASE_,   framework=SCREAMING_SNAKE_CASE_					)
						import torch

						# for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64)
						# makes the export fail randomly
						UpperCAmelCase_:		Optional[int]							      =     torch.zeros_like(inputs["""input_ids"""]					)
						# make every second token global
						UpperCAmelCase_:		Any							      =     1

						return inputs











from typing import TYPE_CHECKING

from ...utils import _LazyModule


a      :  int			     =			{'tokenization_wav2vec2_phoneme': ['Wav2Vec2PhonemeCTCTokenizer']}


if TYPE_CHECKING:
	from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
	import sys

	a      :  int			     =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






